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http://hdl.handle.net/1942/36971
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DC Field | Value | Language |
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dc.contributor.author | Huang, Junting | - |
dc.contributor.author | Meng, Ying | - |
dc.contributor.author | LIU, Feng | - |
dc.contributor.author | Liu , Chang | - |
dc.contributor.author | Li, Huan | - |
dc.date.accessioned | 2022-03-24T08:18:50Z | - |
dc.date.available | 2022-03-24T08:18:50Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-02-24T14:43:28Z | - |
dc.identifier.citation | COMPUTERS & INDUSTRIAL ENGINEERING, 164 (Art N° 107854) | - |
dc.identifier.uri | http://hdl.handle.net/1942/36971 | - |
dc.description.abstract | Inventory control and variation reduction are critical and complicated issues for multistage production processes (MPP) because reasonable inventory is key to ensuring continuous production and on-time order delivery in iron and steel enterprises. However, due to the uncertainties in production environments, physics-based models cannot accurately and efficiently approximate inventory variation propagation in MPP. Moreover, classical statistical models usually fail to consider material production processes with spatio-temporal movement characteristics. Therefore, in this study, a spates-temporal Markov model (STMM) with the probability chain adjustment (STMMPC) is developed to predict states of inventory variation and analyze inventory variation propagation in multistage steel production processes. Firstly, the STMM is established, where the expression of the state transition probability matrices is derived based on both spatial and temporal dimensions. Secondly, probability chains and probabilities of joint states following the chains are defined and used to further improve the prediction accuracy of the STMM. Finally, a differential evolution algorithm with self-adaptive mutation strategies is adopted to optimize the weights of the probabilities in STMMPC. The results based on actual steel production data demonstrate that the STMMPC is superior to STMM and regular Markov models, and the model is relatively stable against changes in the weight parameters. Furthermore, the proposed method can assist managers with better production plans to maintain optimal inventory balance. | - |
dc.description.sponsorship | We thank reviewers and editors, whose constructive suggestions help the presentation of this paper. This research is supported by the Major Program of National Natural Science Foundation of China [71790614], the National Natural Science Foundation of China [72002028], the National Natural Science Foundation of China [72101052], LiaoNing Revitalization Talents Program (XLYC1801009, XLYCYSZX1903), Postdoctoral Science Foundation of China under Grant [2021M700720] and the 111 Project [B16009]. | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.rights | 2021 Elsevier Ltd. All rights reserved. | - |
dc.subject.other | Inventory variation | - |
dc.subject.other | Steel production | - |
dc.subject.other | Multistage production process | - |
dc.subject.other | Spatio-temporal Markov model | - |
dc.subject.other | Differential evolution | - |
dc.title | Modeling and predicting inventory variation for multistage steel production processes based on a new spatio-temporal Markov model | - |
dc.type | Journal Contribution | - |
dc.identifier.volume | 164 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Meng, Y (corresponding author), Northeastern Univ, Frontier Sci Ctr Ind Intelligence & Syst Optimiza, Shenyang 110819, Peoples R China. | - |
dc.description.notes | huangjunting1987@163.com; mengying@ise.neu.edu.cn; feng.liu@uhasselt.be; | - |
dc.description.notes | lc1987328@126.com; magic_vvv@aliyun.com | - |
local.publisher.place | THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 107854 | - |
dc.identifier.doi | 10.1016/j.cie.2021.107854 | - |
dc.identifier.isi | WOS:000752860400002 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Huang, Junting] Northeastern Univ, Minist Educ, Key Lab Data Analyt & Optimizat Smart Ind, Shenyang 110819, Peoples R China. | - |
local.description.affiliation | [Meng, Ying] Northeastern Univ, Frontier Sci Ctr Ind Intelligence & Syst Optimiza, Shenyang 110819, Peoples R China. | - |
local.description.affiliation | [Liu, Feng] Hasselt Univ, Transportat Res Inst, Wetenschapspk 5,Bus 6, B-3590 Diepenbeek, Belgium. | - |
local.description.affiliation | [Liu, Chang] Liaoning Engn Lab Data Analyt & Optimizat Smart I, Shenyang 110819, Peoples R China. | - |
local.description.affiliation | [Liu, Chang] Liaoning Key Lab Mfg Syst & Logist Optimizat, Shenyang 110819, Peoples R China. | - |
local.description.affiliation | [Li, Huan] Baoshan Iron & Steel Co Ltd, Dept Mfg Management, Shanghai 201900, Peoples R China. | - |
local.uhasselt.international | yes | - |
item.fullcitation | Huang, Junting; Meng, Ying; LIU, Feng; Liu , Chang & Li, Huan (2022) Modeling and predicting inventory variation for multistage steel production processes based on a new spatio-temporal Markov model. In: COMPUTERS & INDUSTRIAL ENGINEERING, 164 (Art N° 107854). | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2023 | - |
item.contributor | Huang, Junting | - |
item.contributor | Meng, Ying | - |
item.contributor | LIU, Feng | - |
item.contributor | Liu , Chang | - |
item.contributor | Li, Huan | - |
item.accessRights | Embargoed Access | - |
item.embargoEndDate | 2025-02-28 | - |
crisitem.journal.issn | 0360-8352 | - |
crisitem.journal.eissn | 1879-0550 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Modeling and predicting inventory variation for multistage steel production processes based on a new spatio-temporal Markov model.pdf Restricted Access | Published version | 1.82 MB | Adobe PDF | View/Open Request a copy |
auteursversie.pdf Until 2025-02-28 | Peer-reviewed author version | 2.88 MB | Adobe PDF | View/Open Request a copy |
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